Enhancing Geotechnical Data Workflows through Open-Source Integration

Modern geotechnical practice faces unprecedented challenges in managing increasingly complex and voluminous subsurface datasets while maintaining interoperability across diverse software ecosystems. Traditional document-based and proprietary workflows limit advanced analytics, machine learning applications, and collaborative decision-making. This paper presents a framework for enhancing geotechnical data workflows through open-source technologies, standardized formats, and cloud-based computing platforms. Key elements include automated data validation, Python-based data pipelines, and interactive web-based visualization for real-time collaboration. A real-world case study with the Louisiana Department of Transportation and Development (LADOTD) illustrates practical implementation of these concepts in deep foundation projects, including automated CPT analysis, statistical evaluation of site variability, and 3D subsurface modeling for bridge design. The results demonstrate significant improvements in efficiency, data accessibility, and defensible decision-making, highlighting the transformative potential of integrating open-source and commercial solutions to maximize the value of geotechnical data.